1. Fields of the Invention
The present invention generally relates to OCR (Optical Character Recognition). More particularly, the present invention relates to recognizing a character under a noisy condition.
2. Description of the Prior Art
Optical Character Recognition (OCR) refers to a mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into a machine-editable text. Microsoft Office® Document Imaging (MODI) and Tesseract from Google® are examples of the OCR.
Systems performing the OCR are common and perform character recognition on a wide variety images according to diverse application, e.g., a pattern recognition, artificial intelligence and machine vision. However, a traditional OCR method fails to recognize a photographed character, for example, if the photo of the character is partially obscured or distorted because of dirt or an obstruction on the character photograph.
In order to recognize a character under a noisy or obstructed condition, techniques have developed to train the systems (systems performing the OCR) to recognize parts of characters, e.g., lower half of a character, and uses an elimination (e.g., throwing away upper half of the character) to identify the character. For example, a US Pre-Granted Publication (US 2002/0131642 A1) (hereinafter “'642”) describes that “a method in '642 improves classification accuracy by improving the effectiveness or robustness of the underlying normalized correlation operation; the method partitions each unknown input character into several pre-defined overlapping regions; each region is evaluated independently against a library of template regions; a normalized correlation operation is then performed between the unknown input character region and each of the character template regions defined in the character library”.
The techniques, e.g., the method described in '642, often work well. However, the techniques may produce non-optimal results, because selecting a predefined part of a character may leave some of unobstructed information unused. For example, assume that a system performing the OCR is trained to recognize lower half of a character. Then, when the system receives a character like FIG. 3 (a), the system may not be able to identify whether the character is 3 or 5 by considering only lower half of the character.
Therefore, it would be desirable to have a system and method for recognizing a character with a noise or an obstruction by utilizing all unobstructed information in the character.